Companies may need to measure and ensure the data quality stored in their enterprise database systems. Data quality may be reflected in different aspects. A reasonable measurement of data quality may need to take into account of all these aspects. The measurement of data quality may further depend on several surrounding factors, e.g., security of the database system, which may drastically change the results if ignored.
Existing discrete technologies and algorithms for measuring data quality have not been systematically integrated to take into account of all aspects of data quality. Current data quality assessment services concern themselves only with part of the whole range of data quality aspects. That is, they do not provide a user with a comprehensive assessment of the enterprise data quality. Moreover, they do not offer the flexibility of customizing the assessment to the user's general data environment or to the different scenarios in which the user may want to run the assessment. Furthermore, the results of data quality measurements are not linked to a triggering mechanism that may automatically trigger workflow processes based on the assessment of data quality and pre-determined rules.